Zobrazeno 1 - 10
of 6 250
pro vyhledávání: '"LI, Yanan"'
The problem of roadside monocular 3D detection requires detecting objects of interested classes in a 2D RGB frame and predicting their 3D information such as locations in bird's-eye-view (BEV). It has broad applications in traffic control, vehicle-ve
Externí odkaz:
http://arxiv.org/abs/2404.01064
Autor:
Parashar, Shubham, Lin, Zhiqiu, Liu, Tian, Dong, Xiangjue, Li, Yanan, Ramanan, Deva, Caverlee, James, Kong, Shu
Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive accuracy on ImageNet (60-80%), its performance drops below 10% for more
Externí odkaz:
http://arxiv.org/abs/2401.12425
Long-Tailed 3D Object Detection (LT3D) addresses the problem of accurately detecting objects from both common and rare classes. Contemporary multi-modal detectors achieve low AP on rare-classes (e.g., CMT only achieves 9.4 AP on stroller), presumably
Externí odkaz:
http://arxiv.org/abs/2312.10986
In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neu
Externí odkaz:
http://arxiv.org/abs/2401.10244
Autor:
Qi, Shichao, Liu, Yi, Wang, Ziqiao, Chen, Fucong, Li, Qian, Ji, Haoran, Li, Rao, Li, Yanan, Fang, Jingchao, Liu, Haiwen, Wang, Fa, Jin, Kui, Xie, X. C., Wang, Jian
Disorder is ubiquitous in real materials and can have dramatic effects on quantum phase transitions. Originating from the disorder enhanced quantum fluctuation, quantum Griffiths singularity (QGS) has been revealed as a universal phenomenon in quantu
Externí odkaz:
http://arxiv.org/abs/2311.06710
Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by
Externí odkaz:
http://arxiv.org/abs/2310.19257
Trained on web-scale image-text pairs, Vision-Language Models (VLMs) such as CLIP can recognize images of common objects in a zero-shot fashion. However, it is underexplored how to use CLIP for zero-shot recognition of highly specialized concepts, e.
Externí odkaz:
http://arxiv.org/abs/2310.09929
Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network based learning control schem
Externí odkaz:
http://arxiv.org/abs/2308.04223
Data augmentation for deep learning benefits model training, image transformation, medical imaging analysis and many other fields. Many existing methods generate new samples from a parametric distribution, like the Gaussian, with little attention to
Externí odkaz:
http://arxiv.org/abs/2310.07801
Autor:
Liu, Xiaohong, Min, Xiongkuo, Sun, Wei, Zhang, Yulun, Zhang, Kai, Timofte, Radu, Zhai, Guangtao, Gao, Yixuan, Cao, Yuqin, Kou, Tengchuan, Dong, Yunlong, Jia, Ziheng, Li, Yilin, Wu, Wei, Hu, Shuming, Deng, Sibin, Xiao, Pengxiang, Chen, Ying, Li, Kai, Zhao, Kai, Yuan, Kun, Sun, Ming, Cong, Heng, Wang, Hao, Fu, Lingzhi, Zhang, Yusheng, Zhang, Rongyu, Shi, Hang, Xu, Qihang, Xiao, Longan, Ma, Zhiliang, Agarla, Mirko, Celona, Luigi, Rota, Claudio, Schettini, Raimondo, Huang, Zhiwei, Li, Yanan, Wang, Xiaotao, Lei, Lei, Liu, Hongye, Hong, Wei, Chuang, Ironhead, Lin, Allen, Guan, Drake, Chen, Iris, Lou, Kae, Huang, Willy, Tasi, Yachun, Kao, Yvonne, Fan, Haotian, Kong, Fangyuan, Zhou, Shiqi, Liu, Hao, Lai, Yu, Chen, Shanshan, Wang, Wenqi, Wu, Haoning, Chen, Chaofeng, Zhu, Chunzheng, Guo, Zekun, Zhao, Shiling, Yin, Haibing, Wang, Hongkui, Meftah, Hanene Brachemi, Fezza, Sid Ahmed, Hamidouche, Wassim, Déforges, Olivier, Shi, Tengfei, Mansouri, Azadeh, Motamednia, Hossein, Bakhtiari, Amir Hossein, Aznaveh, Ahmad Mahmoudi
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major cha
Externí odkaz:
http://arxiv.org/abs/2307.09729